Graphs are a powerful way to represent and analyze relationships in data, from social networks to molecular structures. This talk delves into advanced graph-based machine learning techniques using Python libraries like NetworkX, PyTorch Geometric (PyG), and StellarGraph. We’ll cover the fundamentals of graph representation, node embeddings, and graph neural networks (GNNs), along with practical applications such as fraud detection, recommendation systems, and biological research. Attendees will gain insights into how to model real-world problems as graphs, implement efficient algorithms, and leverage graph-based machine learning to extract valuable insights from complex datasets.